Interacting multiple model tracking algorithm fusing input estimation and best linear unbiased estimation filter. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Sheng, Hu; Zhao, Wenbo; Wang, Jingen
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 1(2017)
• Page Start: 70
• Page End: 77
• Publication Date: 2017-01-01
• Subjects: target tracking -- tracking filters -- adaptive signal detection; interacting multiple‐model tracking algorithm -- best linear unbiased estimation filter -- manoeuvring target tracking -- robust tracking -- constant velocity motion -- fusing input estimation -- IMM‐IE -- BLUE filter -- manoeuvre fluctuation -- innovation sequence modification technique -- manoeuver detection -- manoeuver estimation -- modified input estimator -- MIE -- CV motion -- adaptive tracking algorithm; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2015.0657
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In manoeuvring target tracking, a primary tradeoff is the robust tracking of manoeuvres against the accurate tracking of constant velocity (CV) motion. To achieve this goal, an interacting multiple model (IMM) algorithm fusing input estimation (IE) and best linear unbiased estimation (BLUE) filter is presented. First, the constant input assumption of IE is modified to track possible manoeuvre fluctuation. Then, an innovation sequence modification technique is proposed so manoeuvre can be detected and estimated sequentially. With improvements above, a modified input estimator (MIE) is designed to track varying manoeuvres. In view of the optimal tracking of BLUE filter for CV motion, MIE and BLUE filter are fused within IMM framework to generate an overall adaptive tracking algorithm. Simulation results reveal the proposed approach yields better accuracy for CV tracking and robustness for manoeuvre tracking.